Improved Artificial BP Neural Networks and Genetic Algorithm Used to Predict the Purity of the Artificial Synthetic Hydrotalcite

Abstract:

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.

• Periodical: Key Engineering Materials (Volumes 280-283)
• Pages: 495-498
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.280-283.495
• Online since: February 2007
• Authors: Qiang Luo, Qing Li Ren
• Keywords: BP Neural Network (BPNN), Mg,Al-Hydrotalcite, Purity

References

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